code: def collate_fn(self, batch):
      # convert list of dicts to dict of lists
      B = len(batch)
  
      batch = {k: [dic[k] for dic in batch] for k in batch[0]}
  
      # stack for features that already have the same shape
      batch["wav_lengths"] = torch.stack(batch["wav_lengths"])
      batch["text_lengths"] = torch.stack(batch["text_lengths"])
      batch["conditioning"] = torch.stack(batch["conditioning"])
      batch["cond_lens"] = torch.stack(batch["cond_lens"])
      batch["cond_idxs"] = torch.stack(batch["cond_idxs"])
  
      if torch.any(batch["cond_idxs"].isnan()):
          batch["cond_idxs"] = None
  
      if torch.any(batch["cond_lens"].isnan()):
          batch["cond_lens"] = None
  
      max_text_len = batch["text_lengths"].max()
      max_wav_len = batch["wav_lengths"].max()
  
      # create padding tensors
      text_padded = torch.IntTensor(B, max_text_len)
      wav_padded = torch.FloatTensor(B, 1, max_wav_len)
  
      # initialize tensors for zero padding
      text_padded = text_padded.zero_()
      wav_padded = wav_padded.zero_()
      for i in range(B):
          text = batch["text"][i]
          text_padded[i, : batch["text_lengths"][i]] = torch.IntTensor(
              text
          )
          wav = batch["wav"][i]
          wav_padded[
              i, :, : batch["wav_lengths"][i]
          ] = torch.FloatTensor(wav)
  
      batch["wav"] = wav_padded
      batch["padded_text"] = text_padded
      return batch
  
file_path: TTS/TTS/tts/layers/xtts/trainer/dataset.py
imports: import os
  import random
  import sys
  import torch
  import torch.nn.functional
  import torch.utils.data
  from TTS.tts.models.xtts import load_audio
label_desc: Use image node_id 7 for calling the XTTSDataset obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: batch